predictive and reliability o o_sulaiman _revision 2_

Jurnal Mekanikal June 2011, No. 32, 38- 60

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PREDICTIVE AND RELIABILITY BASED COLLISION RISK AVERSION MODEL FOR INLAND WATERWAYS: THE CASE

FREQUENCY ESTIMATION FOR MALAYSIAN LANGAT RIVER

O.O. Sulaiman*1, Ab.Saman Abd. Kader2, A.H. Saharuddin1 and Adi Maimun Abdul Malik2

1 Faculty of Maritime Studies and Marine Science, Universiti Malaysia Terengganu

21030 Kuala Terengganu, Terengganu, Malaysia

2 Marine Technology Department,Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

ABSTRACT Collisions of commercial ships cover the largest part of accidents scenario in waterways. Waterways accidents expose vessel owners and operators, as well as the public to risk. They attract possibility of losses such as vessel cargo damage, injuries, loss of life, environmental damage, and obstruction of waterways. Collision risk is a product of the probability of the physical event its occurrence as well as losses of various nature including economic losses. Environmental problem and need for system reliability call for innovative methods and tools to assess and analyze extreme operational, accidental and catastrophic scenarios as well as accounting for the human element, and integrate these into a design environments part of design objectives. This paper discusses modeling of waterways collision risk frequency in waterways. The analysis consider mainly the waterways dimensions and other related variables of risk factors like operator skill, vessel characteristics, traffic characteristics, topographic, environmental difficulty of the transit, and quality of operator's information in transit which are required for decision support related to efficient, reliable and sustainable waterways developments. 5.3 accidents in 10, 000 years is observed for Langat River, this considered acceptable in maritime and offshore industry, but for a channel using less number of expected traffic, it could be considered high. Providing safety facilities like traffic separation, vessel traffic management could restore maximize sustainable use of the channel. Keywords: Collision, risk, reliability, frequency, inland waterways, environmental

prevention 1.0 INTRODUCTION

Collision in waterways falls under high consequence incidents, collision data may be imperfect or inconstant, making it difficult to account for dynamic issues associated with vessels and waterways requirement. Accounting for these lapses necessitated need to base collision analysis on hybrid use of deterministic, probabilistic or simulation methods depending on the availability of a data. Developing sustainable inland water transportation (IWT) requires transit risk analyses of waterways components and relationship between factors such as environmental conditions, vessel characteristics, operators' information

*Corresponding author : [email protected]

Jurnal Mekanikal, June 2011

39

about the waterway, as well as the incidence of groundings and collisions, using available data. Whatever information is available is useful for risk and reliability based decision work of accidents rate of occurrence, consequence and mitigation [1, 7]. Risk and reliability based design entails the systematic integration of risk analysis in the design process targeting system risk prevention, reduction that meet high level goal and leave allowance for integrated components of the system including environment that will facilitate and support a holistic approach for reliable and sustainable waterways appropriate and require trade-offs and advance decision-making leading to optimal design solutions.

Frequency estimation work on channel lead to fundamental sustainable model of transit risk that include factors such as traffic type and density, navigational aid configuration, channel design and waterway configuration and classification. For cases where there are insufficient historical record to support their inclusion, more comprehensive models of transit risk will have to rely on integral use of hybrid of deterministic, probabilistic, stochastic method whose result could further be simulated or employ expert judgment to optimize deduced result [2]. Risk based collision model are derivative for improvement of maritime accident data collection, preservation and limit acceptability using information relating to the following:

i. Ports for entering incidents, traffic characteristics, frequency of accident, "barge train" movements as well as individual barges

ii. Vessel characteristics, record data on actual draft and trim, presence and use of tugs, presence of pilots.

iii. Environmental condition, wind speed and direction, visibility, water level, current speed and direction, Tide Forecast Error, Real-time Environmental Information etc.

iv. Types of cargo and vessel movements. v. Operator skill, quality of operator's information. vi. Uncertainty in surveys/charts, geographical distribution of transit, topographic

difficulty of the transit, improve temporal resolution (transits by day or hour), eliminate/correct erroneous and duplicate entries (e.g. location information).

This paper describes frequency analysis of risk based model, where accident frequency are determined and matched with waterway variables and parameter. The result hopes to contribute to decision support for development and regulation of inland water transportation.

2.0 BACKGROUND

The study area is Langat River, 220m long navigable inland water that has been under utilized. Personal communication and river cruise survey revealed that collision remain the main threat of the waterways despite less traffic in the waterways. This make the case to establish risk and reliability based model for collision aversion for sustainable development of the waterways a necessity. Data related to historical accidents, transits, and environmental conditions were collected. Accident data are quite few, this is inherits to most water ways and that make probabilistic methods the best preliminary method to analyze the risk which can be optimized through expert rating and simulation methods as required.


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Figure 1: Langat map Barge and tug of capacity 5000T and 2000T are currently plying this waterway at draft of 9 and 15m respectively. Collisions (including contact between two vessels and between a vessel and a fixed structure), causes of collision linked to navigation system failure, mechanical failure and vessel motion failure are considered in this work towards design of safe and reliable the river for transportation. Safety associated with small craft is not taken into account. The next section describes the relevant information relating to channel, vessel and environment employed in the risk process. Lack of information about the distribution of transits during the year, the joint distribution of ship size, flag particular, environmental conditions become main derivative from probabilistic estimation. In total risk management system of various methods is used according to result expectation and performance contribution. The study use Langat River to a case study to test the model, because it is a big River with big potential that is underutilized. The testing of the model on Langat could help decision support for its development and regulation in future [3, 7]. The model described is suitable for preventive safety reliability decision for new water way development. When it is safe the environment is preserved and protected. 3.0 BASELINE DATA

Vessel movement, port call consists of two transits in Langat River: one into and one out of the port. Safe transit data consider the same barge type and size for risk analysis are considered. The required radius of curvature at bends for 5000 DWT, Towed barge Length = Barge Length + Tug Length + Tow Line, R> (4-6) length of barge train to meet the navigation requirement (PIANC, 2007). Water level Mean, water level = 40cm seasonal variation, Existing coastal environmental current. Coastal current, Average Speed in spring tide 0.4 -1.2 m/s, Avg. Speed in Neap 0.2 - 1.0 m/s is considered part of environmental parameters [4, 10].

Table 2: River Langat tributary

Channel Parameters Width Depth

Maneuvering lane Vessel clearance Bank suction Wind effect Current effect

Channel with bends Navigation aids

Pilot Tugs

Draught Trim Squat

Exposure allowance Fresh water adjustment

Allowance maneuvering Overdepth allowance

Depth transition Tidal allowance


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Table 3: River width and depth parameters Figure 2: Channel width parameter Figure 3: Channel straightening and alignment

Table 4: Vessel requirement: Barge parameter Table 5: Vessel requirement: Tug parameter

3.1 Data Collection Limitation Limitations in data collection poised hybrid combinatory use of historical, first principle, or deterministic and stochastic analysis, future data collection effort can open opportunity for improvement in validation analysis as well as understanding of accident risk. In this case the data is good enough data to model a predictive and state space analysis model of frequency of occurrence in the channel. Major data problems are as follows [12]

Design parameter

Approach channel

Straight Bend 98m 120m

3-6m 3-6m

Side slope 10H:1V 10H:1V Estuarine 135.7km North (44.2km) South (9.9km)

Barge parameter 2000 tons 5000 tons

Length (m) 67.3 76.2 Beam (m) 18.3 21.3 Depth (m) 3.7 4.9 Draft (m) 2.9 4.0

Tugs parameter 2000 tons 5000 tons

Length (m) 23.8 23.8 Beam (m) 7.8 7.8 Depth (m) 3.5 3.5 Draft (m) 2.8 2.8 Horse Power (hp) 1200 1200

Channel width : One way TrafficStraight channel = 98m, Bend = 120m, Depth= 6m

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i. Vessel Casualty Data: Inherent problem with causality data have missing entries, duplicate entries, and inaccuracies.

ii. Environmental Data: Limitations are associated with potential change in real-time oceanographic data systems.

iii. Port-Specific Data: information about safe transits counts categorization by flag, vessel type, vessel size, with tug escort and piloting information, taken at hourly by authority.

iv. Surveys and Chart Data: it is important to compare conventional cartographic uncertainty and with new technology to cover additional uncertainties.

4.0 SAFETY AND ENVIRONMENTAL RISK FOR IWT Risk and reliability based model aim to develop innovative methods and tools to assess operational, accidental and catastrophic scenarios. It requires accounting for the human element, and integrates them as required into the design environment. Risk based design entails the systematic integration of risk analysis in the design process. It target safety and environment risk prevention and reduction as a design objective. To pursue this activity effectively, an integrated design environment to facilitate and support a holistic risk approach to ship and channel design is needed. Total risk approaches enable appropriate trade off for advanced sustainable decision making. Waterways accident falls under scenario of collision, fire and explosion, flooding, grounding.

Risk based design entails the systematic risk analysis in the design process targeting risk preventive reduction. It facilitates support for total risk approach to ship and waterways design. Integrated risk based system design requires the availability of tools to predict the safety, performance and system components as well as integration and hybridisation of safety element and system lifecycle phases. Therefore, it becomes imperative to develop, refine, verify, validate reliable model through effective methods and tools. The risk process begins with definition of risk which stands for the measure of the frequency and severity of consequence of an unwanted event (damage, energy, oil spill). Frequency at which potential undesirable event occurs is expressed as events per unit time, often per year. The frequency can be determined from historical data. However, it is quite inherent that event that don’t happen often attract severe consequence and such event are better analyzed through risk based and reliability model. Figure 3.2 shows main components of risk based design for IWT. Risk is defined as product of probability of event occurrence and its consequence. Risk (R) = Probability (P) x Consequence (C) (1)

Incidents are unwanted events that may or may not result to accidents. Necessary

measures should be taken according to magnitude of event and required speed of response should be given. Accidents are unwanted events that have either immediate or delayed consequences. Immediate consequences variables include injuries, loss of life, property damage, and persons in peril. Point form consequences variables could result to further loss of life, environmental damage and financial costs. The earlier stage of the process involves finding the cause of risk, level of impact, destination and putting a barrier by all mean in the pathway. Risk work process targets the following:

i Cause of risk and risk assessment, this involve system description, identifying the

risk associated with the system, assessing them and organising them in degree or matrix. IWT risk can be as a result of the following: (i) Root cause, (ii) Immediate cause, (iii) Situation causal factor, (iv) Organization causal factor.

ii Risk analysis and reduction process, this involve analytic work through deterministic and probabilistic method that strengthen can reliability in system. Reduction process


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that targets initial risk reduction at design stage, risk reduction after design in operation and separate analysis for residual risk for uncertainty as well as human reliability factor.

Uncertainty risk in complex systems can have its roots in a number of factors ranging from performance, new technology usage, human error as well as organizational cultures. They may support risk taking, or fail to sufficiently encourage risk aversion. To deal with difficulties of uncertainty risk migration in marine system dynamic, risk analysis models can be used to capture the system complex issues, as well as the patterns of risk migration. Historical analyses of system performance are important to establish system performance benchmarks that can identify patterns of triggering events, this may require long periods of time to develop and detect. Assessments of the role of human and organizational error, and its impact on levels of risk in the system, are critical in distributed, large scale dynamic systems like IWT couple with associated limited physical oversight. Effective risk assessments and analysis required three parts highlighted in the relation below. Risk modeling = Framework + Models + Process (2)

Reliability based verification and validation of system in risk analysis should be followed with creation of database and identification of novel technologies required for implementation of sustainable system.

4.1 Risk Framework Risk framework provides system description, risk identification, criticality, ranking, impact, possible mitigation and high level objective to provide system with what will make it reliable. The framework development involves risk identification which requires developing understanding the manner in which accidents, their initiating events and their consequences occur. This includes assessment of representation of system and all linkage associated risk related to system functionality and regulatory impact (See Figure 4 a and b)

(a) (b)

Figure 4: IMO Risk framework

Risk framework should be developed to provide effective and sound risk assessment and analysis. The process requires accuracy, balance, and information that meet high scientific standards of measurement. The information should meet requirement to get the science right and getting the right science. The process requires targeting interest of stakeholder including members of the port and waterway community, public officials,


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regulators and scientists. Transparency and community participation helps ask the right questions of the science and remain important input to the risk process, it help checks the plausibility of assumptions and ensures that synthesis is both balanced and informative. Employment of quantitative analysis with required insertion of scientific and natural requirements provide analytical process to estimate risk levels, and evaluating whether various measures for risk are reduction are effective.

4.2 Safety and Environmental Risk and Reliability Model (SERM) There is various risk and reliability tools available for risk based methods that fall under quantitative and qualitative analysis. Figure 5 show the analysis risk model flowchart choice of best methods for reliability objective depends on data availability, system type and purpose. However employment of hybrid of methods of selected tool can always give the best of what is expect of system reliability and reduced risk.

(a) (b)

Figure 5: IMO Risk framework 4.3 SERM Process SERM intend to address risks over the entire life of the complex system like IWT system where the risks are high or the potential for risk reduction is greatest. SERM address quantitatively, accident frequency and consequence of IWT. Other risk and reliability components include human reliability assessment which is recommended to be carried out separately as part of integrated risk process. Other waterways and vessel requirement factors that are considered in SERM model are: (i) Construction (ii), Towing operations and abandonment of ship, (iii) Installation, hook-up and commissioning, and (iv) Development and major modifications

Integrated risk based method combined various technique as required in a process. Table 2 shows available risk based design for techniques. This can be applied for each level of risk. Each level can be complimented by applying causal analysis (system linkage),


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expert analysis (expert rating), and organizational analysis (Community participation) in the risk process. Figure 7 shows stakes holder that should be considered in risk process. From Figure 2, the method use is risk analysis that involves frequency analysis where the system is modeled with hybrid of deterministic, probabilistic and stochastic process. Technically, the process of risk and reliability study involves the following four areas: (i) System definition of high goal objective, (ii) Qualitative hazard identification and assessment, (iii), Quantitative hazard frequency and consequence analysis, (iv) Risk acceptability, sustainability and evaluation.

Table 6: Risk based design techniques

Process Suitable techniques

HAZID HAZOP, What if analysis, FMEA, FMECA

Risk analysis Frequency, consequence, FTA, ETA

Risk evaluation Influence diagram, decision analysis

Risk control option Regulatory, economic, environmental, function elements

matching and iteration

Cost benefit analysis ICAF, Net Benefit

Human reliability Simulation/ probabilistic

Uncertainty Simulation/probabilistic

Risk monitoring Simulation/ probabilistic

The process of risk work can further be broken down into the following elements:

i. Definition and problem identification

ii. Hazard and consequence identification

iii. Analysing the likelihood’s of occurrence

iv. Analyzing consequences

v. Evaluation of uncertainty

vi. Risk control option (RCO) and risk control measure (RCM

vii. Sustainability of (cost safety, environment, injury, fatality, damage to structure, environment) and risk acceptability criteria

viii. Reliability based model verification and validation: statistical software, triangulation, iteration.

ix. Recommendation for implementation: Implement, establishing performance standards to verify that the arrangements are working satisfactorily and continuous monitoring, reviewing and auditing the arrangements

Employment of these benefit provide a rational. Formal environmental protection

structure and process for decision support guidance and monitoring about safety issues. The scope of sustainable risk based design under consideration involves stochastic, analytic and predictive process work leading to avoidance the harms in waterways. Figure 8 shows block diagram of SERM components for IWT. Safety and Environmental Risk and Reliability Model (SERM) for IWT required having clear definition of the following issues:


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i. Personnel and attendance

ii. Identify activities

iii. Vessel accidents including passing vessel accident, crossing , random

iv. Vessel location and waterway geography on station and in transit to shore.

v. Impairment of safety functions through determination of likelihood of loss of key safety functions lifeboats, propulsion temporary refuge being made ineffectiveness by an accident.

vi. Risk of fatalities, hazard or loss of life through measure of harm to people and sickness.

vii. Property damage through estimation of the cost of clean-up and property replacement.

viii. Business interruption through estimation of cost of delays in production.

ix. Environmental pollution may be measured as quantities of oil spilled onto the shore, or as likelihood’s of defined categories of environmental impact or damage to infrastructures. Allowance should be made to introduce new issue defining the boundary in the

port from time to time. The choice of appropriate types of risk tool required for the model depend on the objectives, criteria and parameter that are to be used. Many offshore risk based design model consider loss of life or impairment of safety functions. There is also much focus on comprehensive evaluation of acceptability and cost benefit that address all the risk components. Figure 9 shows the risk and reliability model combined process diagram. The analysis is a purely technical risk analysis. When the frequencies and consequences of each modelled event have been estimated, they can be combined to form measures of overall risk including damage, loss of life or propulsion, oil spill. Various forms of risk presentation may be used. Risk to life is often expressed in two complementary forms. The risk experienced by an individual person and societal risk. The risk experienced by the whole group of people exposed to the hazard (damage or oil spill).

Accident and incident are required to be prevented not to happen at all. The consequence of no safety is a result of compromise to safety leading to unforgettable loses and environmental catastrophic. Past engineering work has involved dealing with accident issues in reactive manner. System failure and unbearable environmental problem call for new proactive ways that account for equity requirement for human, technology and environment interaction. The whole risk assessment and analysis process starts with system description, functionality and regulatory determination and this is followed by analysis of: (i)Fact gathering for understanding of contribution factor (ii), Fact analysis of check consistency of accident history, (iii) Conclusion drawing about causation and contributing factor, (iv) Countermeasure and recommendation for prevention of accident

Most risk based methods define risk as: Risk = Probability (Pa) x Consequence (Ca) (3)

or in a more elaborate expression risk can be defined as:

Risk = Threat x Vulnerability x {direct (short-term) consequences + (broad) (4)

Consequences}


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In risk analysis, serenity and probability of adverse consequence hazard are deal with through systematic process that quantitatively measure , perceive risk and value of system using input from all concerned waterway users and experts. Risk can also be expressed as:

Risk = Hazard x Exposure (5) Where hazard is anything that can cause harm (e.g. chemicals, electricity, Natural

disasters), while exposure is an estimate on probability that certain toxicity will be realized. Severity may be measured by No. of people affected, monetary loss, equipment downtime and area affected by nature of credible accident. Risk management is the evaluation of alternative risk reduction measures and the implementation of those that appear cost effective where:

Zero discharge or negative damage = Zero risk (6)

The risk and reliability model subsystem in this thesis focus on the following identified

four risks assessment and analysis application areas that cover hybrid use of technique

ranging from qualitative to qualitative analysis (John, 2000): (i) Failure Modes

Identification Qualitative Approaches, (ii) Index Prioritisation Approaches, (iii) Portfolio

Risk Assessment Approaches, and (iv) Detailed Quantitative Risk Assessment

Approaches.

5.0 COLLISIONS RISK MODELLING Collision in waterways is considered low frequency and high consequence events that have associative uncertainty characteristics / component of dynamic and complex physical system. This makes risk and reliability analysis the modest methods to deal with uncertainties that comes with complex systems. Employment of hybrid deterministic, probabilistic and stochastic method can help break the barriers associated with transit numbers data and other limitation. Conventionally, risk analysis work often deal with accident occurrence, while the consequence is rarely investigated, addressing frequency and consequence analyze can give clear cuts for reliable objectives. Risk and reliability based design can be model by conducting the analysis of following elements of risk process [13, 15]:

i. Risk identification ii. Risk analyses iii. Damage estimation iv. Priotization of risk level v. Mitigation vi. Repriotization of exposure category: mitigate risk or consequence of events that

meet ALARP principle. vii. Reassess high risk events for monitoring and control plans. viii. Recommendation, implementation, continuous monitoring and improvement.

Collision is likely to be caused by the following factors shown in Figure 7 derived from fault three analyses from RELEX software. The relex software is based on fault three analysis where consequence of causal events are add up through logic gate to give minimum cut set probability that trigger the event. It is more effective for subsystem analysis.


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P (collision) = P (propulsion failure) + P (loss of navigation failure) + (7) P (Loss of vessel motion)

There is also causes are mostly as a result of causes from external sources like small craft, are cause of cause, cause from other uncertainty including human error may attract separate subsystem analysis.

5.1 Collision Data Collision data are drawn from relevant marine administrator; there is expectation that most data gaps can be covered by the probability estimations. The Langat River work model risk through systemic analysis procedures for sustainable inland waterways transportation. It determine the probability of failure or occurrence, risk ranking, damage estimation, high risk to life safety, cost benefit analyze, sustainability and acceptability criteria [5, 14]. The study analyze causal accidental relating to navigational, mechanical failure and human error and ignored those identified as intentional for barge and tugs of 5000T and 2000T having respective drift of draft greater than 9 to 15m. Table 7, 8 and 9 shows some of the annual traffic summary, collision and the consequences on Langat. Seasonal trends can be stochastically modeled from probabilistic result, environmental condition and traffic volume fluctuation is also considered negligible. For visibility, navigation is considered to be more risky at night than day time, the analysis follow generic assumption for evenly safe distribution evenly during day and night.

Figure 6: Collision contributing factors Figure 7: Tugs puling barge in Langat

A critical review of risk assessment methodologies applicable to marine systems reiterate that the absence of data should not be used as an excuse for not taking an advantage of the added knowledge that risk assessment can provide on complex systems [6]. Approximation of the risks associated with the system can provide a definition of data requirements. The treatment of uncertainty in the analysis is important, and the limitations of the analysis must be understood. However, data management system and better approach can always accommodate little data or no data. Table 6 shown models that have been used design of system based on risks in marine industry.

IMO and Sirkar et al (1997) methods lack assessment of the likelihood of the event, likewise other model lack employment of stochastic method whose result could cover uncertainties associated with dynamic components of channel and ship failure from causal factors like navigational equipment, training and traffic control [14]. Therefore, combination of stochastic, statistical and reliability method based on combination of probabilistic, goal based, formal safety assessment, deterministic methods and fuzzy method using historical data of waterways, vessel environmental, first principle deterministic and traffic data can deliver best outcome for predictive, sustainable, efficient and reliable model for complex and dynamic system like inland water transportation. The general hypothesis behind assessing physical risk model is that the probability of an


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accident on a particular transit depends on a set of risk variables require for analysis of prospective reliable design. Figure 8 shows traffic data utilized in the model. Most of the method above used historical data, the novel method in this paper used limited data of traffic used to model the physics of the system, the transfer function and stochastically project accident frequency. The projection is generic and can be used for any waterways and it consider random collision not which is not considered by previous model.

Table 6 : Previous risk work

Table 7: Tug boat and vessel activities along river for 2008

Table 8: Vessel traffic

Model Application Drawback Brown et al (1996) Environmental Performance of

Tankers

Sirkar et al (1997)

Consequences of collisions and groundings

Difficulties on quantifying consequence metrics

Brown and Amrozowicz

Hybrid use of risk assessment, probabilistic simulation and a spill consequence assessment model

Oil spill assessment limited to use of fault three

Sirkar et al (1997) Monte Carlo technique to estimate damage and spill cost analysis for environmental damage

Lack of cost data

IMO (IMO 13F 1995)

Pollution prevention index from probability distributions damage and oil spill.

Lack (Sirkar et al, 1997)). rational

Research Council Committee(1999)

Alternative rational approach to measuring impact of oil spills

Lack employment of stochastic probabilistic methods

Prince William Sound,Alaska, (PWS (1996)

The most complete risk assessment Lack of logical risk assessment framework (NRC,1998))

Volpe National Transportation Center (1997)).

Accident probabilities using statistics and expert opinion.

Lack employment of stochastic methods

Puget Sound Area (USCG (1999).

Simulation or on expert opinion for cost benefit analysis

Clean up cost and environmental damage omission

Jetty 3 nos. Daily 9 times.

Weekly 63 times. Monthly 252 times. Annually 3024 times.

Total number of barge Time Traffic 12 Every day (24 hrs.)

6 (every 4 hrs) Incoming 6 (every 4 hrs) outgoing


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Table 9: Common to traffic

5.2 Traffic Frequency Estimation Modeling Traffic density of meeting ship

Traffic density of meeting ship: ρ = W

Nm

..τν hips/�� (8)

Where Nm is number of ships frequenting the channel, v is speed of the ship, T= time of traffic activities per annum and W is width of the channel.

Figure 8: 5000 barge data and Langat waterway

5.3 Analysis of Present Situation Traffic situation: Below are representation of various collision situations for head- on, overtaking and crossing (angle) collision scenario (see Figure 9). Where: B1 = mean beam of meeting ship (m), V1 = mean speed of meeting ship (knots), B2 = beam of subject ship (m), V2 = speed of subject ship (knots), Nm = arrival frequency of meeting ships (ship/time), D= relative sailing distance. Expected number of collision Ni= 9.6.B.D.ρρρρ s 1/passage. (9)

(a) overtaking (b) passing cases (c) Random Figure 9 Collision situations

All Speed 2 – 3 knots Traffic All single way traffic

Lay -bys Proposed four locations for Lay-bys


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Table 10 and 11 show relevant data from previous analysis used for approximation.

Table 10: Expression for collision situation [8, 11]

Approximations: L=6B, D=W, Ni= Pi (10) Necessary period for ship to pass the fairway T=D/v = 3000/3 = 1000 sec (11)

Table 11: Failure per nautical mile and failure per passage for different waterways [8]

Fairway cµ (failure per nautical

mile or hour)

Pc(failure per passage or encounter)

UK 2.5 x10-5 1.x10-4 US 1.5 x10-5 1.4.x10-5

Japan 3.0 x10-5 1.3.x10-5

Therefore average Pc and cµ = 2.5 x 10-5 for random (12)

Probability of loosing navigation control within the fairway Pc = Tc⋅µ failure / passage (13)

Probability of collision Pa= (Pi. Pc collision / passage) (14)

Collision per annual (Na) = Pa. Nm Collision per year (15)

In the frequency analysis, the annual frequency of each failure case is estimated.

Separate frequencies are estimated for each operating phase as required. In modelling the development, consequences and impact of the events, each failure case is split into various possible outcomes. the outcomes are the end events on an event tree or chain of event trees. Each outcome probability is estimated by combining the probabilities for appropriate branches of the event tree. The outcome frequency (Fo ) is then:

FO = Fe ∏ Pb (16)

Expression Head – on Overtaking Random

Basic

4 x B X D X ρS W

BB )( 21 + .

21

21(

VV

VV

⋅+

. D. Nm Ni = V

N

.τ . ( π4

L+2*B)

Standardised

4 x B X D X ρS

W

BB )( 21 + .

21

21

VV

VV

⋅+

. D. Nm

9.6.D. ρn

� B

Relative 1 1 2.4


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Where, Fe is failure frequency, Pb probability of one segment. Not all possible outcomes are modeled. Representative scenarios are selected for modeling, and the scenario frequency is taken as:

FS = ∑outcomes

OF (17)

Failure per nautical mile and failure per passage can be selected from previous

representative work. Necessary period for ship to pass the fairway T=D/v = 3000/3 = 1000 sec. The result of accident frequency (Fa) can be compare with acceptability criteria for maritime industry. If it is two high the system could be recommended to implement TSS. If the result is high TSS can be model to see possible reduction due to its implementation.Table 12 shows frequency risk acceptability criteria for maritime and offshore industry.

Table 12: Frequency acceptability criteria

5.4 Frequency Analysis Result This result indicates that the collision in Langat is not risk on ALARP graph. Accident per year of 5.3E-5 is observed for current 3 number of vessel operating at speed of 3 knot. But physical observation revealed that there is significant and exception increase in collision that needs to be address for a channel with less traffic density. It is also observed from the plot of frequency Vs speed that when traffic density is changing traffic density of 5 and 6 and speed up to 5 considered to be cause high risk of accident frequency in the waterway (See Figure 10). (a) (b)

Figure 10: Accident frequency Vs at changing number of ship

Frequency classes Quantification

Very unlikely once per 1000 year or more likely

Remote once per 100- 1000 year

Occasional once per 10- 100 year

Probable once per 1- 10 years

Frequent more often than once per year

0.0E+00

1.0E-04

2.0E-04

3.0E-04

4.0E-04

5.0E-04

6.0E-04

7.0E-04

8.0E-04

9.0E-04

1.0E-03

1.1E-03

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Fa

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um

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co

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pe

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ar

V :-Velocity

Fa vs VV=+1 D=W=3B B=21.3 M=+500 Nm=x

Nm2

Nm3

Nm4

Nm5

Nm6

0.0E+00

5.0E-07

1.0E-06

1.5E-06

2.0E-06

2.5E-06

3.0E-06

3.5E-06

4.0E-06

4.5E-06

5.0E-06

5.5E-06

6.0E-06

6.5E-06

7.0E-06

7.5E-06

8.0E-06

8.5E-06

9.0E-06

9.5E-06

1.0E-05

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Fa :-

Exp

ecte

d nu

mbe

r of c

ollIs

ion

per y

ear

V :-Velocity

Fa vs VV=+1 D=W=3B B=21.3 M=+500 Nm=x

Nm2

Nm3

Nm4

Nm5

Nm6


53

Figure 11 shows accident frequency at changing width and beam of the channel. Risk is acceptable for accident per 10, 000 year, if proposed maintenance of channel improvement plan is implemented. Beam and wide have linear relationship (3B=W). (a) (b)

Figure 11: Accident frequency Vs beam and width of the channel The maximum speed is round 10 knot for width of 64m and probability of 1/1000 years, other speed above this are intolerable. As width of the channel decrease there is higher risk -> Accident frequency probability increase. The maximum width considered for Langat River is 64; this width is considered too small and risky for the channel for accident per 1000 years. Different speed should be advised to ship for such situation. Width of channel can change as a result of erosion. Increasing channel width to 250m could allow speed of 20 knot at acceptable Fa (Na) of 1x10E-4. Ship operating at Langat at 3 knot at River Langat, is considered not high risk for accident per 100, 000 years. The regression equation for the trend is represented by y is 2E-08x + 1E-05 @ R² is 1. Similar trend is observe for Figure 12b, the beam and width are related according to PIANC W=3B AND L=6B. Table 14 shows regression equations for the frequency analysis.

Figure 12(a) and (b) shows cross plotting of the channel variable, both plots are the same; the defense is that Figure 12(b) is logged because of large number shows the risk level for all channel parameters variables (speed, width, number of ships, and beam of ship). It is observed that the maximum of ship can up to 4, at the point where speed and Number of ship curves meet, provided all channel and vessel safety parameters are in place.

(a)

0.0E+00

1.0E-05

2.0E-05

3.0E-05

4.0E-05

5.0E-05

6.0E-05

7.0E-05

8.0E-05

9.0E-05

1.0E-04

1.1E-04

1.2E-04

1.3E-04

1.4E-04

1.5E-04

1.6E-04

1.7E-04

1.8E-04

1.9E-04

2.0E-04

17 18 20 21 23 24 26 27 29 30 32 33 35 36 38 39 41

Fa :-

Exp

ecte

d n

um

ber o

f collIs

ion

per yea

r

B:- Beam

Fa vs B

B=16.8+0.5

0.0E+00

1.0E-06

2.0E-06

3.0E-06

4.0E-06

5.0E-06

6.0E-06

7.0E-06

8.0E-06

9.0E-06

1.0E-05

1.1E-05

1.2E-05

1.3E-05

1.4E-05

1.5E-05

Fa :-

Expecte

d n

um

ber of

collIs

ion

per ye

ar

W :- Width

Fa vs W

v=10

v=20

v=30

v=40

v=50

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

Accid

ent fr

eque

ncy

Speed

Combined graph

V change

W change

NM change

B change


54

(b)

Figure 12: Cross plotting of channel variables (speed, width, number of ships, and beam of ship)

Table 13: Regression equation for Frequency analysis

6.0 UNCERTAINTY AND SYSTEM COMPLEXITY ANALYSIS 6.1 Subsystem Level Analysis For total risk work the following analysis could perform separately as part of subsystem risk level analysis include (i) power transmission (loss of propulsion), (ii) navigation (loss of mooring function and (iii) human reliability, subsystem level analysis can be facilitated by using frequency calculation through Fault Tree Analysis (FTA) modeling involve top down differentiation of event to branches of member that cause them or participated in the causal chain action and reaction. While consequence calculation can be done by using Event Tree Analysis (ETA), where probability is assigned to causal factor leading to certain event in the event tree structure. 6.2 Channel Complexity Analysis Channel complexity that could be addressed in the risk and reliability work are visibility weather, squat, bridge, river bent and human reliability. Figure 19 show channel complexity for Langat. Poor visibility and the number of bend may increase in the risk of and collisions. A model extracted from Dover waterway studies concluded with the following: Fog Collision Risk Index (FCRI) = ( P1+ VI1+ P2+ VI2+ P3 . VI3 ) (18)

Fa @Nm changing Speed

y = 2E-05e-0.11x R² = 0.826 Exponential

Fa @V y = 2E-05e-0.11xR² = 0.826 R² = 1 Square Fa W y = 2E-08x + 1E-05 R² = 1 Square Fa B y = 9E-07x + 0.000 R² = 0.999 Linear

1.0E-07

1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

Fa :-E

xpec

ted

num

ber o

f col

lIsio

n pe

r yea

r

V :-Velocity

Fa vs V

B change (16.8+.5)W change (50.4+1.5)Nm (+1)V (+1)


55

Where: = Probability of collision per million encounters, = Fraction of time that the visibility is in the range k, K = Visibility range: clear (>4km), Mist/Fog (200m- 4km), Tick/dense (less than 200m). Empirically derived means to determine the relationship between accident risk, channel complexity parameters and VTS is given by equation : R = -0.37231 - 35297C + 16.3277N + 0.2285L -0.0004W + 0.01212H + 0.0004M (19)

For predicted VTS consequence of 100000 transit, C = 1 for an open approach area and 0 otherwise, N = 1 for a constricted waterway and 0 otherwise, L = length of the traffic route in statute miles, W = average waterway/channel width in yards, H = sum of total degrees of course changes along the traffic route, M = number of vessels in the waterway divided by L.

Barge movement creates very low wave height and thus will have insignificant impact on river bank erosion and generation of squat event. Speed limit can be imposed by authorities for wave height and loading complexity. Human reliability analysis is also important to be incorporated in the channel; complexity risk work, this can be done using questionnaire analysis or the technique of human error rate prediction THERP probabilistic relation

PEA = HEPEA � CWFPS

m

k kk +∑ = ⋅⋅1 (20)

Where: PEA = Probability of error for specific action,HEPEA = Nominal operator error probability for specific error, PSFK = numerical value of kth performance sapping factor, WK= weight of PSFK (constant), m=number of PSF, C= Constant. 6.3 Reliability Based Validation Reliability analysis is designed to cater for uncertainty and to provide confident on the model. It is important for this to be carried out separately. Reliability work could include projection for accident rate for certain number of year the following techniques: (1) Accident mean, variance and standard deviation from normal distribution For 10 years =>Mean (µ ) = 10 x Na (21)

Variance (σ ) = 10 x Na x (1-Na), Standard deviation = σ , Z = (X-µ ) / σ (22) (2) Stochiatic process using poison distribution, Year for system to fail from binomial, mean time to failure and poison distribution, or determination of exact period for next accident using binomial function. Ship collisions are rare and independent random event in time. The event can be considered as poison events where time to first occurrence is exponentially distributed. Fr ( N/γ , T ) = eγ. T( ) γ , ( γ.T )N). N! (23) Binomial distribution – for event that occurs with constant probability P on each trail, the likelihood of observing k event in N trail is binomial distribution.

L(K/N,P)= )(ΡΝ

p k (1-P)N.K (24)


56

Where average number of occurrence is NP. (3) Comparing the model behaviour apply to other rivers of relative profile and vessel particular. (4). Triangulating analysis of sum of probability of failure from subsystem level failure analysis. And (5) Implementation of TSS is one of the remedies for collision risk observed and predicted in Langat; this can be done through integration of normal distribution along width of the waterways and subsequent implementation frequency model. And the differences in the result can reflect improvement derived from implementation of TSS.

(x) =

1

21 12

( )2

e xµµ π

−− (25)

(x) =

1

21 12

( )2

e xµµ π

−− (26)

(3) Safety level and cost sustainability analysis. Figure 13 shows the best accident frequency that is acceptable,. Ct is is the total cost, Co is the cost of damage, and Cc is the cost of repair.

Figure 13: Risk cost benefit analysis

6.4 Validation Result Validation and reliability analysis of the model yield the following result. Figure 14 shows accident frequency residual plot from Minitab is shown with good fitness. Figure 15: Shows accident consequence validations, accident consequence good to fit to the method, residual graph of Cumulative Density Function (CDF) profile tracing infinity. In this analysis Frequency is refer to as Fa or Na.

Figure 14: Accident frequency residual plot Figure 15: Accident consequence validation

Co Cc & Ct vs Fa

0

50000000

100000000

150000000

200000000

250000000

2.80

E-05

3.14

E-05

3.51

E-05

3.89

E-05

4.29

E-05

4.71

E-05

5.16

E-05

5.62

E-05

6.10

E-05

6.60

E-05

7.13

E-05

7.67

E-05

8.23

E-05

8.81

E-05

9.41

E-05

1.00

E-04

1.07

E-04

1.13

E-04

1.20

E-04

1.27

E-04

1.34

E-04

1.42

E-04

1.49

E-04

1.57

E-04

1.65

E-04

Cos

t

Co

Cc

Ct


57

0.000090.000080.000070.000060.000050.000040.000030.00002

99

95

90

80

70

60

50

40

30

20

10

5

1

FA_NM CHANGE

Percent

Mean 0.00005357

StDev 0.00001331

N 50

AD 0.821

P-Value 0.032

Probability Plot of FA_NM CHANGENormal

Figure 16 shows residual histograms distribution diagram for accident frequency, skewed to low risk area, outlier can be removed.

Figure 16: Residual histograms distribution diagram for accident frequency

Figure 17(a) Shows Log normal plots Accident frequency (Na), distribution shows a good to fit. Curve Figure 17(b) also show a very good curve fit for the model.

(a) (b)

Figure 17 : Log normal plot Accident frequency (Na)

Figure 18 shows process reliability capability, the fitting of the curve revealed the reliability of the frequency model.

1.00E-08 1.00E-07 1.00E-06 1.00E-05 1.00E-04 1.00E-03

1

5

10

20

3040506070

80

90

95

99

Data

Per

cent

AD* 2.18

Goodness of Fit

Lognormal base e Probability Plot for NaML Estimates - 95% CI

Location

Scale

-5.86094

0.764552

ML Estimates


58

40200

0.00020

0.00015

0.00010

0.00005

0.00000

1007550

302010

V

FA

W B

Matrix Plot of FA vs V, W, B

50454035302520151051

0.00020

0.00015

0.00010

0.00005

0.00000

Observation

FA

Number of runs about median: 2

Expected number of runs: 26.0

Longest run about median: 25

Approx P-Value for Clustering: 0.000

Approx P-Value for Mixtures: 1.000

Number of runs up or down: 1

Expected number of runs: 33.0

Longest run up or down: 49

Approx P-Value for Trends: 0.000

Approx P-Value for Oscillation: 1.000

Run Chart of FA

0.00

020

0.00

016

0.00

012

0.0000

8

0.0000

4

0.0000

0

-0.00004

LSL USL

LSL 3e-007

Target *

USL 0.0002

Sample Mean 6.73842e-006

Sample N 50

StDev(Within) 3.4414e-006

StDev(Overall) 2.7649e-005

Process Data

Cp 9.67

CPL 0.62

CPU 18.72

Cpk 0.62

Pp 1.20

PPL 0.08

PPU 2.33

Ppk 0.08

Cpm *

Overall Capability

Potential (Within) Capability

PPM < LSL 100000.00

PPM > USL 0.00

PPM Total 100000.00

Observed Performance

PPM < LSL 30681.49

PPM > USL 0.00

PPM Total 30681.49

Exp. Within Performance

PPM < LSL 407933.86

PPM > USL 0.00

PPM Total 407933.86

Exp. Overall Performance

Within

Overall

Process Capability of FA

2000

1000

0Individual Value

464136312621161161

100

50

0

Moving Range

Normality Test

(Anderson-Darling)

Results Fail Pass

P-value < 0.005 0.078

Original Transformed

I-MR Chart (transformed)

Confirm that the process is stable.

Normality Plot (lambda = -0.50)

The points should be close to the line.

Capability Analysis for FA

Diagnostic Report

60

HighLow

Z.Bench = 1.11

0.000200.000160.000120.000080.000040.00000-0.00004

LSL USL

Actual (overall) capability is what the customer experiences.

spec limits.

percentage of parts from the process that are outside the

-- The defect rate is 13.28%, which estimates the

Conclusions

Upper Spec 0.0002

Target *

Lower Spec 3e-007

Customer Requirements

Mean 6.738E-06

Standard deviation 2.765E-05

Actual (overall) capability

Pp 0.54

Ppk 0.41

Z.Bench 1.11

% Out of spec 13.28

PPM (DPMO) 132809

Process Characterization

Actual (overall) Capability

Are the data inside the limits?

Comments


Summary Report

How capable is the process?

(a) (b)

Figure 18: Process capability

Figure 19 shows the matrix plot for the model, the safe areas for the variable workability are shown in the matrix plot.

(a) (b)

Figure 19: Matrix plot Figure 20 a, b, and c shows the capability report for the model.

(a) (b)

50

25

0

0.000080.000060.00004

1007550 0.00020.00010.0000

120

80

40

35

25

15

0.0002

0.0001

0.0000

40200

0.00008

0.00006

0.00004

302010

V

W

B

FA

FA_NM CHANGE

Matrix Plot of V, W, B , FA, FA_NM CHANGE


59

!

i

!Stability

eliminate any special cause variation in your process before continuing with this analysis.

stable, examine the control charts on the Diagnostic Report. Investigate out-of-control points and

Stability is an important assumption of capability analysis. To determine whether your process is

Subgroups

Number of

sources of process variation when collected over a long enough period of time.

You have 50 subgroups. For a capability analysis, this is usually enough to capture the different

Normality

estimates should be reasonably accurate.

The transformed data passed the normality test. As long as you have enough data, the capability

of Data

Amount

observations becomes smaller.

reasonably precise capability estimates. The precision of the estimates decreases as the number of

The total number of observations is less than 100. You may not have enough data to obtain

Check Status Description


Report Card

(c)

Figure 20: Log normal plot Accident frequency (Na)

7.0 CONCLUSIONS Hybrid of deterministic, statistical, historical, probabilistic and stochastic method along with channel and vessel profile baseline data has been used to model accident possibility in waterway in order to meet condition for safe transits, and environmental conditions for inland waterway. Factors such as vessel type and size, traffic density, speed and visibility conditions are major risk factor of accidents the probabilistic method represent reliable method to develop models for safety and environmental prevention and collision accident risk aversion who precedence is could be short term (damage) or long term (impact of oil outflow) environmental impact. Accident collision per number of year has been determined for potential decision support for limit definition for number of ship, speed, required width and beam of ship. Variables that affect accident rates have been simulated for necessary limit acceptability purpose for the channel. Accident rate has increased compare to previous year, a situation that required attention for solution. Advantage of implementing of TSS in respect to beam requirement is also presented. Implications of concept of uncertainty can help also on decision support relating to navigational aids and transit regulations for poor visibility conditions as well has employment of improved navigation systems, such as electronic charts, GPS receivers, and VTS, to mitigate causal factors.

REFERENCES 1. Yacov T. Haimes. 1998. Risk Modeling, Assessment and Management. John Wiley &

Sons, INC. Canada pp. 159 - 187. 2. Amrozowicz, M.D. 1996. The Quantitative Risk of oil Tanker Groundings. Master’s

degree thesis, Ocean Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts.

3. Department of Environment, Modeling and data integration in the study of sediment, 2000, Kuala Lumpur, Malaysia.

4. Kielland, P., Tubman, T., 1994. On estimating map model errors and GPS position errors. Ottawa, Canada: Canadian Hydrographic Service.

5. DnV. 2001, Marine Risk Assessment, Her majesty stationary office United Kingdom. 6. Sirkar, J., Ameer, P., Brown, A., Goss, P., Michel, K., Nicastro, F. and Willis, W. 1997.

A Framework for Assessing the Environmental Performance of Tankers in Accidental Groundings and Collisions. SNAME Transactions


60

7. David Vose, 1996. Risk Analysis – A Quantitative Guide. John Wiley & Sons, INC. canada pp. 67-87.

8. Fujii Y. 1982. Recent Trends in Traffic Accidents in Japanese Waters. Journal of Navigation. Vol35 (1), pp. 88- 102

9. Millward, A. 1990. A Preliminary Design Method for the Prediction of Squat in Shallow Water. Marine Technology 27(1):10-19.

10. John X. Wang. 2000. What Every Engineer Should Know about Risk Engineering and Management. Markel Deker Inc, Switzerland, pp. 112-128.

11. Lewison, Gr. G., 1978. The Risk Encounter Leading to Collision. Journal of Navigation, Vol 31 (3), pp. 288- 109.

12. M. Moderras. 1993. What Every Engineer Should Know about Reliability and Risk Analysis. MarkelDeker Inc, Switzerland, pp. 299-314.

13. DnV, BV, SSPA, 2002, Thematic Network for Safety Assessment of Waterborne Transportation.

14. PV Varde, ASrividya, VVs Sanyasi Rao, Ashok Chauhan. 2006. Reliability, Safety, and Hazard – Adavnced Informed Technology. Narosa Publishing House, India, PP339.

15. John McGregor. 2004. (BV report), Pollution Prevention and Control.

predictive and reliability o o_sulaiman _revision 2_

Documents

frequency analysis of

risk andreliability

waterways accidents

inland waterways

collision risk aversion

system risk prevention

waterways requirement

obstruction of waterways